Author：Chunhui Gao

Reviewer：Ying Ge、Junyi Shen

需求描述Description of requirements

用R从形式上复现原图。Use R to formally reproduce the original drawing

出自Fromhttps://academic.oup.com/jnci/article-lookup/doi/10.1093/jnci/djy156

Figure 1. Plot of all alterations detected by plasma next-generation sequencing (n=210). Size of circles represents number of patients identified with an alteration.

图的解析Analysis of the figure

例文用来展示基因上的 SNP/Indel/CNV 突变（多态性差异）。 例如，在TP53基因中发现了最多的多态性，包括 CNV 差异 SPLICE 8 个，单核苷酸位点差异 R273H 5个等。用点的大小表示差异，所以很容易发现常见的多态性差异。Example to show SNP/Indel/CNV mutations (polymorphic differences) on genes. For example, the most polymorphisms were found in the TP53 gene, including 8 CNV differential SPLICEs, 5 single nucleotide locus differences R273H, etc. The size of the dots represents the difference, so it's easy to spot common polymorphic differences.

稍作观察便可发现，本图是一个“圆环套圆环”的布局，中心处在中央，下一级的项目分别处在外环。图中只有二环，如果要扩展成五环，多显示几个层次应该也不错。A little observation shows that this diagram is a layout of "ring set and ring", with the center in the center and the next level of projects in the outer ring. There are only two rings in the picture, and if you want to expand it to five rings, it should be good to show a few more levels.

原文用的是在线工具The original text uses online tools

推测原文是用这个工具画的：FuncTreehttps://bioviz.tokyo/functree/，能画出类似的图，用来展示基因组数据的关系。

It is speculated that the original text was drawn with this tool: FuncTreehttps://bioviz.tokyo/functree/, which can draw a similar diagram to show the relationship between genomic data.

缺点：输入数据格式复杂非常复杂。需要针对每一个点做有针对性的设置。感兴趣的小伙伴去尝试一下吧～Cons: The input data format is complex and very complex. Targeted settings need to be made for each point. Interested friends go and try it~

如何用R实现How to implement it in R

发现Findtidytuesday2019-11-12的图跟原图很像The picture is very similar to the original picture，代码codehttps://github.com/spren9er/tidytuesday/blob/master/tidytuesday_201946_cran_packages.r，输入数据Enter datahttps://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-11-12/loc_cran_packages.csv。

参考这套代码，用 ggraph 来画这个图。为此，花了几天时间仔细研究了ggraph包，写下了一篇长文Refer to this set of code and use ggraph to draw this graph. To do this, spent a few days perusing the ggraph package and wrote a long article：一文读懂 ggraph 的使用Understand the use of ggraph in one article

应用场景Application scenarios

展示层级结构。例如Show the hierarchy. For example：

注concentrate：

环境设置Environment settings

加载R包Load the R package

library(clusterProfiler)
## 
## clusterProfiler v3.16.1  For help: https://guangchuangyu.github.io/software/clusterProfiler
## 
## If you use clusterProfiler in published research, please cite:
## Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
## 
## Attaching package: 'clusterProfiler'
## The following object is masked from 'package:stats':
## 
##     filter
library(GOplot)
## Loading required package: ggplot2
## Loading required package: ggdendro
## Loading required package: gridExtra
## Loading required package: RColorBrewer
library(tidyverse)
## ─ Attaching packages ──────────────────── tidyverse 1.3.0 ─
## ✓ tibble  3.0.4     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.0
## ✓ purrr   0.3.4
## ─ Conflicts ───────────────────── tidyverse_conflicts() ─
## x dplyr::combine()  masks gridExtra::combine()
## x dplyr::filter()   masks clusterProfiler::filter(), stats::filter()
## x dplyr::lag()      masks stats::lag()
## x purrr::simplify() masks clusterProfiler::simplify()
library(data.table)
## 
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
## 
##     between, first, last
## The following object is masked from 'package:purrr':
## 
##     transpose
library(ggraph)
library(tidygraph)
## 
## Attaching package: 'tidygraph'
## The following object is masked from 'package:stats':
## 
##     filter
Sys.setenv(LANGUAGE = "en") #显示英文报错信息Displays the error message in English
options(stringsAsFactors = FALSE) #禁止chr转成factor Prohibiting chr from converting to factor

加载自定义函数Load custom functions

source(file = "gather_graph_node.R")
source(file = "gather_graph_edge.R")

输入文件的获得 the acquisition of input files

如果你的数据已经整理成very_easy_input.csv的格式，就可以跳过这步，直接进入“输入文件预处理”。If your data has been organized into very_easy_input.csv formats, you can skip this step and go straight to "Input File Preprocessing".

先用clusterProfiler做KEGG的GSEA，然后用例图的形式展示结果。First, use clusterProfiler to do KEGG's GSEA, and then display the results in the form of example diagrams.

gsym.fc <- read.table("easy_input_rnk.txt", header = T)
dim(gsym.fc)
head(gsym.fc)

#把gene symbol转换为ENTREZ ID Convert gene symbol to ENTREZ ID
#此处物种是人，其他物种的ID转换方法，请参考FigureYa52GOplot For the ID conversion method of other species, please refer to FigureYa52GOplot
gsym.id <- bitr(gsym.fc$SYMBOL, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = "org.Hs.eg.db")

#让基因名、ENTREZID、foldchange对应起来Let the gene name, ENTREZID, and foldchange correspond

gsym.fc.id <- merge(gsym.fc, gsym.id, by="SYMBOL", all=F)

#按照foldchange排序Sort by foldchange
gsym.fc.id.sorted <- gsym.fc.id[order(gsym.fc.id$logFC, decreasing = T),]

#获得ENTREZID、foldchange列表，做为GSEA的输入Get the ENTREZID and foldchange list as inputs for GSEA
id.fc <- gsym.fc.id.sorted$logFC
names(id.fc) <- gsym.fc.id.sorted$ENTREZID

#这一条语句就做完了KEGG的GSEA分析This statement completes KEGG's GSEA analysis
kk <- gseKEGG(id.fc, organism = "hsa")
dim(kk)

#把ENTREZ ID转为gene symbol，便于查看通路里的基因Convert the ENTREZ ID to a gene symbol to make it easier to view genes in the pathway
kk.gsym <- setReadable(kk, 'org.Hs.eg.db', #物种species
                    'ENTREZID')

#可以用kk.gsym作为输入，用clusterProfiler画图You can use kk.gsym as input to draw a picture with clusterProfiler
#用法看这里See here for usage https://yulab-smu.github.io/clusterProfiler-book/chapter12.html
#gsym.fc.l <- gsym.fc$logFC
#names(gsym.fc.l) <- gsym.fc$SYMBOL
#cnetplot(sortkk, foldChange = gsym.fc.l, circular = TRUE)

#按照enrichment score从高到低排序，取前5（up）和后5（down）Sort by enrichment score from high to low, take the top 5 (up) and the last 5 (down)
#sortkk <- kk.gsym[order(kk.gsym@result$enrichmentScore, decreasing = T),][c(1:5, (nrow(kk.gsym)-5):nrow(kk.gsym)),]
#这里提取感兴趣的3个通路，数量太多拥挤的话不好看基因名Here are the 3 pathways of interest, and if the number is too crowded, it is not easy to see the gene name
sortkk <- kk.gsym[kk.gsym@result$Description %like% "DNA" | 
                    kk.gsym@result$Description %like% "cycle" | 
                    kk.gsym@result$Description %like% "p53",]

#把富集分析结果整理为GOplot所需的格式Organize the enrichment analysis results into the format required by GOplot
go <- data.frame(Category = "KEGG",
                 ID = sortkk$ID,
                 Term = sortkk$Description, 
                 Genes = gsub("/", ", ", sortkk$core_enrichment), 
                 adj_pval = sortkk$p.adjust)

#基因变化倍数Genetic change folds
genelist <- data.frame(ID = gsym.fc.id$SYMBOL, logFC = gsym.fc.id$logFC)

#把富集分析和倍数整合在一起Integrate enrichment analysis and multiples
circ <- circle_dat(go, genelist)
head(circ)
#可以用circ作为输入，用GOplot画图You can use circ as input and draw with GOplot
#用法看这里See here for usagehttps://wencke.github.io/
#GOCircle(circ)

#保存到文件Save to file
write.csv(circ[,c(3,5,6)],"very_easy_input.csv", quote = F, row.names = F)

输入文件预处理Input file preprocessing

very_easy_input.csv，这里以上面的富集分析结果为例，展示通路和通路里的基因变化倍数FC。三列依次是通路-基因-倍数，可以自由替换成“应用场景”里其他需要展示的信息。Here we take the enrichment analysis results above as an example to show the pathway and the gene change fold FC in the pathway. The three columns are pathway-gene-multiple, which can be freely replaced with other information that needs to be displayed in the "application scenario"

gene_special.txt，要突出显示的基因。第一列是基因名，第二列是类型（例如基因家族信息）Genes to highlight. The first column is the gene name, and the second column is the type (e.g., gene family information)。

df <- read.csv("very_easy_input.csv")
head(df)
##              term genes    logFC
## 1 DNA replication  MCM5 2.010821
## 2 DNA replication  MCM2 1.956162
## 3 DNA replication  MCM6 1.439526
## 4 DNA replication  MCM4 1.422334
## 5 DNA replication  FEN1 1.385611
## 6 DNA replication  RFC4 1.266485
geneSpecial <- read.table("gene_special.txt", header = T)
geneCol <- geneSpecial$Type
names(geneCol) <- geneSpecial$Gene
geneCol
##    MCM6    MCM2     CD6    CDK4   CHEK2   CHEK1 
## "type1" "type1" "type2" "type2" "type3" "type3"

图由两个部分组成，节点（node）和边（edge）。The graph consists of two parts, the node and the edge

要从上面的数据框中采集节点和边的信息。To collect node and edge information from the above data frame

为此，我分别写了两个函数：gather_graph_node() 和 gather_graph_edge() 来完成这一个任务（前面已加载）。To do this, I wrote two functions separately: gather_graph_node() and gather_graph_edge() to accomplish this one task (loaded earlier)

这两个函数的参数设置借鉴了 treemap() 的实现方式。The parameter settings of these two functions are borrowed from the implementation of treemap().

为了确保 node.name 的唯一性，在图中使用了长名，而把原有的名字放在 node.short_name 中去了。To ensure the uniqueness of the node.name, long names are used in the diagram, and the original names are placed in node.short_name.

node.level 则用来指示节点应该处于第几个圆环。is used to indicate that the node should be in the circle

节点的属性统一以 node 作为前缀，而边的属性则以 edge 作为前缀。The properties of nodes are prefixed with node, while the properties of edges are prefixed with edge.

nodes <- gather_graph_node(df, index = c("term", "genes"), value = "logFC", root="all")
## `summarise()` ungrouping output (override with `.groups` argument)
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(dots)` instead of `dots` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## `summarise()` regrouping output by 'term' (override with `.groups` argument)
edges <- gather_graph_edge(df, index = c("term", "genes"), root = "all")
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(index)` instead of `index` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
## `summarise()` ungrouping output (override with `.groups` argument)
nodes <- nodes %>% mutate_at(c("node.level","node.branch"),as.character)
head(nodes, 10)
##                node.name  node.size node.level node.count       node.short_name
## 1                    all 115.692338        all          1                   all
## 2             Cell cycle  70.226424       term         46            Cell cycle
## 3        DNA replication  21.413204       term         21       DNA replication
## 4  p53 signaling pathway  24.052710       term         16 p53 signaling pathway
## 5        Cell cycle/BUB1   1.185457      genes          1                  BUB1
## 6       Cell cycle/BUB1B   1.809404      genes          1                 BUB1B
## 7       Cell cycle/CCNA2   2.446934      genes          1                 CCNA2
## 8       Cell cycle/CCNB1   2.046811      genes          1                 CCNB1
## 9       Cell cycle/CCNB2   3.125239      genes          1                 CCNB2
## 10      Cell cycle/CCNE1   1.609160      genes          1                 CCNE1
##              node.branch
## 1                    all
## 2             Cell cycle
## 3        DNA replication
## 4  p53 signaling pathway
## 5             Cell cycle
## 6             Cell cycle
## 7             Cell cycle
## 8             Cell cycle
## 9             Cell cycle
## 10            Cell cycle
head(edges, 10)
## # A tibble: 10 x 2
##    from            to                   
##    <chr>           <chr>                
##  1 all             Cell cycle           
##  2 all             DNA replication      
##  3 all             p53 signaling pathway
##  4 DNA replication DNA replication/MCM5 
##  5 DNA replication DNA replication/MCM2 
##  6 DNA replication DNA replication/MCM6 
##  7 DNA replication DNA replication/MCM4 
##  8 DNA replication DNA replication/FEN1 
##  9 DNA replication DNA replication/RFC4 
## 10 DNA replication DNA replication/PCNA
# 把要突出显示的基因类型信息加到nodes里Add the gene type information you want to highlight to the nodes
nodes$color <- "normal"
nodes[nodes$node.short_name %in% geneSpecial$Gene,]$color <- geneCol[nodes[nodes$node.short_name %in% geneSpecial$Gene,]$node.short_name]
nodes[nodes$node.short_name %in% geneSpecial$Gene,]
##                      node.name node.size node.level node.count node.short_name
## 20             Cell cycle/CDK4 0.7449589      genes          1            CDK4
## 23            Cell cycle/CHEK1 2.2365719      genes          1           CHEK1
## 24            Cell cycle/CHEK2 0.6951341      genes          1           CHEK2
## 32             Cell cycle/MCM2 1.9561624      genes          1            MCM2
## 36             Cell cycle/MCM6 1.4395262      genes          1            MCM6
## 53        DNA replication/MCM2 1.9561624      genes          1            MCM2
## 57        DNA replication/MCM6 1.4395262      genes          1            MCM6
## 79  p53 signaling pathway/CDK4 0.7449589      genes          1            CDK4
## 81 p53 signaling pathway/CHEK1 2.2365719      genes          1           CHEK1
## 82 p53 signaling pathway/CHEK2 0.6951341      genes          1           CHEK2
##              node.branch color
## 20            Cell cycle type2
## 23            Cell cycle type3
## 24            Cell cycle type3
## 32            Cell cycle type1
## 36            Cell cycle type1
## 53       DNA replication type1
## 57       DNA replication type1
## 79 p53 signaling pathway type2
## 81 p53 signaling pathway type3
## 82 p53 signaling pathway type3
nodes$color <- factor(nodes$color, levels = unique(nodes$color))

# 有了节点和边的数据，使用 `tbl_graph()` 便可以得到一个图。With the data of nodes and edges, you can use 'tbl_graph()' to get a graph.
graph <- tbl_graph(nodes, edges)

开始画图Start drawing

自定义配色，直接出图Custom color scheme is directly illustrated

# 用 `ggraph` 出图Use 'ggraph' to get the graph
gc <- ggraph(graph, layout = 'dendrogram', circular = TRUE) + 
  # 使用 filter 参数去掉 root（前面设置为"all"）节点及与其相连的边Use the filter parameter to remove the root (previously set to "all") node and the edges connected to it
  geom_edge_diagonal(aes(color = node1.node.branch,
                         filter=node1.node.level!="all"), 
                     alpha = 1/3,edge_width=1) + 
  geom_node_point(aes(size = node.size, 
                      color = node.branch,
                      filter=node.level!="all"), alpha = 1/3) + 
  scale_size(range = c(0.5,80)) + #做均一化处理，让点的大小介于range之间Do a homogenization process so that the size of the dots is between ranges
  theme(legend.position = "none") + #不画图例 I don't draw legends

  # 点和边的配色Color matching of dots and edges
  # 如果要改变边的配色，需要同时给边和点变色，否则会对应不上If you want to change the color scheme of the edges, you need to change the color of the edges and points at the same time, otherwise they will not correspond
  scale_edge_color_brewer(palette = "Set1") + #用?scale_color_brewer查看更多配色方案Use? scale_color_brewer see more color schemes
  scale_color_brewer(palette = "Set1") +
  
  # 添加周围注释文字，此处是基因名gene Add the surrounding annotation text, here is the gene name
  geom_node_text(
    aes(
      x = 1.048 * x, #控制字跟点的距离Control the distance between the word and the point
      y = 1.048 * y, #控制字跟点的距离Control the distance between the word and the point
      label = node.short_name,
      angle = -((-node_angle(x, y) + 90) %% 180) + 90,
      filter = leaf,
      color = node.branch
      ),
    size = 6, hjust = 'outward') +
  
  # 添加内环文字，此处是通路名Add the inner ring text, here is the channel name term
  geom_node_text(
    aes(label=node.short_name,
        filter = !leaf & (node.level != "all"),
        color = node.branch),
    fontface="bold",
    size=6,
    family="sans"
  ) + 
  theme(panel.background = element_rect(fill = NA)) +
  coord_cartesian(xlim=c(-1.3,1.3),ylim = c(-1.3,1.3)) #扩大坐标系Expand the coordinate system

gc

ggsave("ccgraph_color.pdf", width = 14, height = 14)

在上面的图形中，线条的颜色由 In the graphic above, the color of the lines is determined bygeom_edge_diagonal(aes(color = node1.node.branch)) 指定designate。

node1.node.branch 指的是出发点（node1）的 node.branch 属性。如果要改变线条颜色，可以修改 nodes 表，添加一个属性（如 color） ，然后在 geom_edge_diagonal() 中将其映射到 color 上即可。

node1.node.branch refers to the node.branch property of the starting point (node1). If you want to change the line color, you can modify the nodes table, add a property (like color), and map it to color in geom_edge_diagonal().

按例文配色，然后后期加背景色Color according to the example, and then add background color later

gc1 <- ggraph(graph, layout = 'dendrogram', circular = TRUE) + 
  #画连线Draw a line
  geom_edge_diagonal(aes(color = node2.color,
                         filter=node1.node.level!="all"), 
                     alpha = 0.5, #透明度transparency
                     edge_width=2.5) + #连线的粗细The thickness of the wires
  scale_edge_color_manual(values = c("#61C3ED","red","purple","darkgreen")) + #自定义颜色Custom colors

  #画点Draw dots
  geom_node_point(aes(size = node.size,
                      filter=node.level!="all"), 
                  #alpha = 1/3,
                  color = "#61C3ED") + #统一为淡蓝色Unified in light blue
  scale_size(range = c(0.5,80)) + #做均一化处理，让点的大小介于range之间Do a homogenization process so that the size of the dots is between ranges
  theme(legend.position = "none") + #不画图例I don't draw legends
  
  # 添加周围注释文字，此处是基因名gene Add the surrounding annotation text, here is the gene name
  geom_node_text(
    aes(
      x = 1.05 * x, #控制字跟点的距离Control the distance between the word and the point
      y = 1.05 * y, #控制字跟点的距离Control the distance between the word and the point
      label = node.short_name,
      angle = -((-node_angle(x, y) + 90) %% 180) + 90,
      filter = leaf
      ),
    color="black", #统一为黑色字Unified as black characters
    size = 6, hjust = 'outward') +
  
  # 添加内环文字，此处是通路名term Add the inner ring text, here is the channel name term
  geom_node_text(
    aes(label=node.short_name,
        filter = !leaf & (node.level != "all")
        ),
    color="black", #统一为黑色字Unified as black characters
    fontface="bold",
    size=6,
    family="sans"
  ) + 
  theme(panel.background = element_rect(fill = NA)) + #背景透明色Background transparent color
  coord_cartesian(xlim=c(-1.3,1.3),ylim = c(-1.3,1.3)) #扩大坐标系Expand the coordinate system


gc1

后期处理Post-processing

保存到pdf文件，是矢量图，可以用Illustrator等软件编辑图形、文字和背景Saved to a pdf file, it is a vector image that can be edited with software such as Illustrator to edit graphics, text, and backgrounds

ggsave("ccgraph.pdf",width = 14,height = 14)

多层嵌套Multi-layer nesting

这套代码不仅可以画两层的图，理论上支持更多层（要不然怎么叫“圆环套圆环”呢？）This set of code can not only draw a two-layer diagram, but theoretically support more layers (otherwise how can it be called a "ring set of rings"?). ）。

下面是一个例子，这里使用了常见的微生物组数据集（这是一个随机生成的 OTU 表）。Here's an example where a common microbiome dataset is used (this is a randomly generated OTU table)

#随机生成一套数据A random set of data is generated
n <- 1000
microbiome <- data.frame(
  otu = paste("OTU",1:n,sep="_"),
  phylum = sample(paste("phylum",1:5,sep="_"),n,replace = T),
  class = sample(paste("class",6:30,sep="_"),n,replace=T),
  order = sample(paste("order",31:80,sep="_"),n,replace = T),
  value = runif(n,min=1,max=1000)
)
head(microbiome)

#保存到文件，便于小白套用格式Save to a file for easy application of formatting

write.csv(microbiome, "microbiome.csv", quote = F, row.names = F)

加载输入数据Load the input data

microbiome.csv，想画几层就给几+1列。这里前4列对应4层，最后一列是最底层节点对应的数值。If you want to draw a few layers, give a few columns. Here, the first 4 columns correspond to 4 layers, and the last column corresponds to the values of the lowest node.

microbiome <- read.csv("microbiome.csv", header = T)
index_micro <- c("phylum","class","order") #除了最低层以外的列名Column names other than the lowest level
nodes_micro <- gather_graph_node(microbiome,index=index_micro,
                                  root = "bacteria") #root名字自己随便取Take the name yourself
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'phylum' (override with `.groups` argument)
## `summarise()` regrouping output by 'phylum', 'class' (override with `.groups` argument)
edges_micro <- gather_graph_edge(microbiome,index=index_micro,root = "bacteria")
## `summarise()` ungrouping output (override with `.groups` argument)

画图Drawing

graph_micro <- tbl_graph(nodes_micro,edges_micro)
ggraph(graph_micro,layout = "dendrogram",circular=T) +
  geom_edge_diagonal(aes(color = node1.node.branch,filter=node1.node.level!="bacteria", alpha = node1.node.level),edge_width=1) + 
  geom_node_point(aes(size = node.size, color = node.branch,filter=node.level!="bacteria"), alpha = 1/3) + 
  scale_size(range = c(0.5,80)) + #做均一化处理，让点的大小介于range之间Do a homogenization process so that the size of the dots is between ranges
  theme(legend.position = "none")+ #不画图例I don't draw legends
  
  scale_edge_color_brewer(palette = "Set1") + #用?scale_color_brewer查看更多配色方案Use? scale_color_brewer see more color schemes
  scale_color_brewer(palette = "Set1") +

  # 添加周围注释文字，此处是基因名gene Add the surrounding annotation text, here is the gene name
  geom_node_text(
    aes(
      x = 1.058 * x, #控制字跟点的距离Control the distance between the word and the point
      y = 1.058 * y, #控制字跟点的距离Control the distance between the word and the point
      label = node.short_name,
      angle = -((-node_angle(x, y) + 90) %% 180) + 90,
      filter = leaf,
      color = node.branch
      ),
    size = 1, hjust = 'outward') +
  
  # 添加内环文字，此处是通路名term Add the inner ring text, here is the channel name term
  geom_node_text(
    aes(label=node.short_name,
        filter = !leaf & (node.level == "phylum"),
        color = node.branch),
    fontface="bold",
    size=6,
    family="sans"
  ) + 
  theme(panel.background = element_rect(fill = NA)) +
  coord_cartesian(xlim=c(-1.3,1.3),ylim = c(-1.3,1.3)) #扩大坐标系Expand the coordinate system

ggsave("ccgraph_microbiome.pdf", width = 14, height = 14)

参考资料Resources

搞大你的点Make your point biggerhttps://mp.weixin.qq.com/s/JNIncz3W-59yjGk2ibJWUw

sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] tidygraph_1.2.0        ggraph_2.0.4           data.table_1.13.4     
##  [4] forcats_0.5.0          stringr_1.4.0          dplyr_1.0.2           
##  [7] purrr_0.3.4            readr_1.4.0            tidyr_1.1.2           
## [10] tibble_3.0.4           tidyverse_1.3.0        GOplot_1.0.2          
## [13] RColorBrewer_1.1-2     gridExtra_2.3          ggdendro_0.1.22       
## [16] ggplot2_3.3.2          clusterProfiler_3.16.1
## 
## loaded via a namespace (and not attached):
##   [1] fgsea_1.14.0         colorspace_2.0-0     ellipsis_0.3.1      
##   [4] ggridges_0.5.2       qvalue_2.20.0        fs_1.5.0            
##   [7] rstudioapi_0.13      farver_2.0.3         urltools_1.7.3      
##  [10] graphlayouts_0.7.1   ggrepel_0.9.0        bit64_4.0.5         
##  [13] AnnotationDbi_1.50.3 fansi_0.4.1          scatterpie_0.1.5    
##  [16] lubridate_1.7.9.2    xml2_1.3.2           splines_4.0.2       
##  [19] GOSemSim_2.14.2      knitr_1.30           polyclip_1.10-0     
##  [22] jsonlite_1.7.2       broom_0.7.3          GO.db_3.11.4        
##  [25] dbplyr_2.0.0         ggforce_0.3.2        BiocManager_1.30.10 
##  [28] compiler_4.0.2       httr_1.4.2           rvcheck_0.1.8       
##  [31] backports_1.2.1      assertthat_0.2.1     Matrix_1.3-0        
##  [34] cli_2.2.0            tweenr_1.0.1         htmltools_0.5.0     
##  [37] prettyunits_1.1.1    tools_4.0.2          igraph_1.2.6        
##  [40] gtable_0.3.0         glue_1.4.2           reshape2_1.4.4      
##  [43] DO.db_2.9            fastmatch_1.1-0      Rcpp_1.0.5          
##  [46] enrichplot_1.8.1     Biobase_2.48.0       cellranger_1.1.0    
##  [49] vctrs_0.3.6          xfun_0.19            rvest_0.3.6         
##  [52] lifecycle_0.2.0      DOSE_3.14.0          europepmc_0.4       
##  [55] MASS_7.3-53          scales_1.1.1         hms_0.5.3           
##  [58] parallel_4.0.2       yaml_2.2.1           memoise_1.1.0       
##  [61] downloader_0.4       triebeard_0.3.0      stringi_1.5.3       
##  [64] RSQLite_2.2.1        S4Vectors_0.26.1     BiocGenerics_0.34.0 
##  [67] BiocParallel_1.22.0  rlang_0.4.9          pkgconfig_2.0.3     
##  [70] evaluate_0.14        lattice_0.20-41      labeling_0.4.2      
##  [73] cowplot_1.1.0        bit_4.0.4            tidyselect_1.1.0    
##  [76] plyr_1.8.6           magrittr_2.0.1       R6_2.5.0            
##  [79] IRanges_2.22.2       generics_0.1.0       DBI_1.1.0           
##  [82] pillar_1.4.7         haven_2.3.1          withr_2.3.0         
##  [85] modelr_0.1.8         crayon_1.3.4         utf8_1.1.4          
##  [88] rmarkdown_2.6        viridis_0.5.1        progress_1.2.2      
##  [91] grid_4.0.2           readxl_1.3.1         blob_1.2.1          
##  [94] reprex_0.3.0         digest_0.6.27        gridGraphics_0.5-1  
##  [97] stats4_4.0.2         munsell_0.5.0        viridisLite_0.3.0   
## [100] ggplotify_0.0.5